LLUMO AI Experiment is a purpose-built platform designed to systematically test, evaluate, and debug at 10X faster rate. It enables AI teams to move beyond intuition-based development by providing use case based Evals, various AI models and fine-tuning options, helping businesses to build AI systems at a much faster rate.
If your AI development process faces challenges like:
The Quick Start section is designed to help you get up and running with LLUMO AI’s evaluation tools in the fastest and most efficient way. Whether you’re setting up for the first time or integrating evaluations into production, these guides provide step-by-step instructions tailored to different use cases.
LLUMO AI’s Experiment Playground allows you to tailor the evaluation process to meet your unique requirements. Whether you’re analyzing customer service data, academic content, or any other dataset, this powerful tool enables users to assess AI-generated outputs against tailored metrics and criteria that align with their specific needs and objectives.
Unlike pre-defined evaluation methods, which may not fully capture the nuances of different use cases, LLUMO AI’s evaluation allows users to define their own parameters, ensuring the evaluation process is both relevant and effective.
This guide will walk you through the steps of using Experiment playground to perform a detailed and personalized evaluation of your datasets.
Option 1: Upload File
Option 2: Upload via API
Option 3: Import from Hugging Face
Hugging Face is a platform for sharing and accessing machine learning models and datasets.
A dataset is a collection of data used for training or evaluation.
A subset refers to a specific portion of the dataset.
A tree may refer to a hierarchical structure within the dataset or model.
Option 4: Manually
Here, you can create a custom dataset by starting with a blank dataset.
Here, you can follow these steps:
Note: You can customize your evaluations to align with your unique use case and industry requirements.
Select your Evaluation model from the drop-down list.
Select the prompt you want to evaluate.
“Pass & Fail” criteria
Set Rule for KPIs: Each KPI allows you to set a specific threshold to define a “pass” or “fail.”
Example for confidence: If the confidence score is more than 50, it passes.
Tailor evaluation metrics and configurations to suit your specific use case
This Quick Start guide ensures you can navigate LLUMO AI’s evaluation framework effortlessly, saving time and enhancing the quality of your LLM deployments.
Integrating LLUMO AI’s evaluation API into your codebase allows for seamless automation of LLM performance tracking, enabling large-scale and efficient evaluations. Follow these steps to get started:
Set Up API Access
Evaluate LLMs Using API
Make Your First API Call
Create Evaluation Analytics API
This guide ensures a smooth and efficient integration process, allowing you to optimize model performance effortlessly.
Q. What is LLUMO AI Experiment?
LLUMO AI Experiment is a powerful platform designed to help teams test, evaluate, and optimize their LLM applications efficiently. It provides end-to-end tracking, customizable evaluation metrics, and structured experimentation workflows.
Q. How does LLUMO AI Experiment help improve LLM performance?
It allows users to:
Q. What evaluation metrics are available in LLUMO AI Experiment?
LLUMO AI offers:
Q. How do I integrate LLUMO AI’s API into my workflow?
Q. Can I evaluate bulk datasets with LLUMO AI?
Yes! LLUMO AI allows you to evaluate large datasets using multiple key metrics to assess LLM performance at scale.
Guide to Bulk Evaluation
Q. Does LLUMO AI support OpenAI and Vertex AI models?
Yes, LLUMO AI supports evaluation for:
Q. How can I track my experiments over time?
LLUMO AI Experiment includes an Experiment Management feature that logs all test runs, allowing you to compare different configurations and monitor long-term performance.
Q. How do I create analytics for my evaluation runs?
LLUMO AI provides an Evaluation Analytics API that generates performance insights and trends.
Create Eval Analytics API
LLUMO AI Experiment is a purpose-built platform designed to systematically test, evaluate, and debug at 10X faster rate. It enables AI teams to move beyond intuition-based development by providing use case based Evals, various AI models and fine-tuning options, helping businesses to build AI systems at a much faster rate.
If your AI development process faces challenges like:
The Quick Start section is designed to help you get up and running with LLUMO AI’s evaluation tools in the fastest and most efficient way. Whether you’re setting up for the first time or integrating evaluations into production, these guides provide step-by-step instructions tailored to different use cases.
LLUMO AI’s Experiment Playground allows you to tailor the evaluation process to meet your unique requirements. Whether you’re analyzing customer service data, academic content, or any other dataset, this powerful tool enables users to assess AI-generated outputs against tailored metrics and criteria that align with their specific needs and objectives.
Unlike pre-defined evaluation methods, which may not fully capture the nuances of different use cases, LLUMO AI’s evaluation allows users to define their own parameters, ensuring the evaluation process is both relevant and effective.
This guide will walk you through the steps of using Experiment playground to perform a detailed and personalized evaluation of your datasets.
Option 1: Upload File
Option 2: Upload via API
Option 3: Import from Hugging Face
Hugging Face is a platform for sharing and accessing machine learning models and datasets.
A dataset is a collection of data used for training or evaluation.
A subset refers to a specific portion of the dataset.
A tree may refer to a hierarchical structure within the dataset or model.
Option 4: Manually
Here, you can create a custom dataset by starting with a blank dataset.
Here, you can follow these steps:
Note: You can customize your evaluations to align with your unique use case and industry requirements.
Select your Evaluation model from the drop-down list.
Select the prompt you want to evaluate.
“Pass & Fail” criteria
Set Rule for KPIs: Each KPI allows you to set a specific threshold to define a “pass” or “fail.”
Example for confidence: If the confidence score is more than 50, it passes.
Tailor evaluation metrics and configurations to suit your specific use case
This Quick Start guide ensures you can navigate LLUMO AI’s evaluation framework effortlessly, saving time and enhancing the quality of your LLM deployments.
Integrating LLUMO AI’s evaluation API into your codebase allows for seamless automation of LLM performance tracking, enabling large-scale and efficient evaluations. Follow these steps to get started:
Set Up API Access
Evaluate LLMs Using API
Make Your First API Call
Create Evaluation Analytics API
This guide ensures a smooth and efficient integration process, allowing you to optimize model performance effortlessly.
Q. What is LLUMO AI Experiment?
LLUMO AI Experiment is a powerful platform designed to help teams test, evaluate, and optimize their LLM applications efficiently. It provides end-to-end tracking, customizable evaluation metrics, and structured experimentation workflows.
Q. How does LLUMO AI Experiment help improve LLM performance?
It allows users to:
Q. What evaluation metrics are available in LLUMO AI Experiment?
LLUMO AI offers:
Q. How do I integrate LLUMO AI’s API into my workflow?
Q. Can I evaluate bulk datasets with LLUMO AI?
Yes! LLUMO AI allows you to evaluate large datasets using multiple key metrics to assess LLM performance at scale.
Guide to Bulk Evaluation
Q. Does LLUMO AI support OpenAI and Vertex AI models?
Yes, LLUMO AI supports evaluation for:
Q. How can I track my experiments over time?
LLUMO AI Experiment includes an Experiment Management feature that logs all test runs, allowing you to compare different configurations and monitor long-term performance.
Q. How do I create analytics for my evaluation runs?
LLUMO AI provides an Evaluation Analytics API that generates performance insights and trends.
Create Eval Analytics API